
import warnings
warnings.filterwarnings('ignore')

import requests
from bs4 import BeautifulSoup

import pandas as pd
import numpy as np


movie_names = []
years_of_release = []
content_ratings = []
durations = []
genres = []
ratings = []
metascores = []
descriptions = []
directors = []
stars_list = []
votes_list = []
gross_list = []


url_template = "https://www.imdb.com/search/title/?groups=top_1000&sort=user_rating,desc&count=100&start={start}&ref_=adv_nxt"

headers = {
    'Accept-Language': 'en-US,en;q=0.9',  
}

total_titles = 1000

for start in range(1, total_titles + 1, 100):
    url = url_template.format(start=start)
    response = requests.get(url, headers=headers)
    soup = BeautifulSoup(response.content, 'html.parser')
    
    data = soup.findAll('div', attrs= {'class': "lister-item mode-advanced"})
    
    for item in data:
        movie_name = item.h3.a.text
        movie_names.append(movie_name)

        year_release = item.find('span', class_='lister-item-year text-muted unbold').text.strip('()')
        years_of_release.append(year_release)

        content_rating_element = item.p.find('span', class_='certificate')
        content_rating = content_rating_element.text if content_rating_element else "N/A"
        content_ratings.append(content_rating)

        duration = item.p.find('span', class_='runtime').text.replace(' min', '')
        durations.append(duration)

        genre = item.p.find('span', class_='genre').text.strip().replace('\n', '')
        genres.append(genre)

        rating = item.find('div', class_='inline-block ratings-imdb-rating').find('strong').text
        ratings.append(rating)

        metascore = item.find('span', class_='metascore').text.strip() if item.find('span', class_='metascore') else "N/A"
        metascores.append(metascore)

        description = item.find('p', class_='text-muted').find_next_sibling('p').text.strip()
        descriptions.append(description)

        directors_stars = item.find('p', class_='').text.split('|')
        director = directors_stars[0].split(':')[1].strip()
        directors.append(director)

        star = directors_stars[1].split(':')[1].strip().split(', ')
        cleaned_star = [s.strip() for s in star if s.strip() != '']
        stars_list.append(cleaned_star)

        votes_element = item.find('span', class_='text-muted', string='Votes:')
        vote = votes_element.find_next_sibling('span', attrs={'name': 'nv'}).text if votes_element else None
        votes_list.append(vote)

        gross_element = item.find('span', class_='text-muted', string='Gross:')
        gross = gross_element.find_next_sibling('span', attrs={'name': 'nv'}).text if gross_element else None
        gross_list.append(gross)


data_dict = {
    "Movie Name": movie_names,
    "Year of Release": years_of_release,
    "Content Rating": content_ratings,
    "Duration": durations,
    "Genre": genres,
    "Rating": ratings,
    "Metascore": metascores,
    "Description": descriptions,
    "Director": directors,
    "Stars": stars_list,
    "Votes": votes_list,
    "Gross": gross_list
}

df = pd.DataFrame(data_dict)
df


df.to_csv('top_1000_imdb_movies_data.csv', index=False)


import ast
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud
%config InlineBackend.figure_format='retina'
sns.set_theme(style='ticks')

plt.rcParams["axes.spines.right"] = False
plt.rcParams["axes.spines.top"] = False


original_df = pd.read_csv('top_1000_imdb_movies_data.csv')
print(original_df.shape)
original_df.head()

(1000, 12)


df = original_df.copy()


df.describe()


df.describe(include='object')


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 12 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   Movie Name       1000 non-null   object 
 1   Year of Release  1000 non-null   object 
 2   Content Rating   984 non-null    object 
 3   Duration         1000 non-null   int64  
 4   Genre            1000 non-null   object 
 5   Rating           1000 non-null   float64
 6   Metascore        846 non-null    float64
 7   Description      1000 non-null   object 
 8   Director         1000 non-null   object 
 9   Stars            1000 non-null   object 
 10  Votes            1000 non-null   object 
 11  Gross            808 non-null    object 
dtypes: float64(2), int64(1), object(9)
memory usage: 93.9+ KB


print(f"Rows with 'Year Of Release' more than 4 characters: {len(df[df['Year of Release'].str.len() > 4])}")

df[df['Year of Release'].str.len() > 4].head(10)

Rows with 'Year Of Release' more than 4 characters: 35


count_converted = 0  

for i, v in enumerate(df['Year of Release']):
    if len(v) > 4:
        df.loc[i, 'Year of Release'] = v[-4:]
        count_converted += 1  

print(f"Converted {count_converted} values in 'Year of Release' column.")

Converted 35 values in 'Year of Release' column.


df['Year of Release'] = df['Year of Release'].astype(int)


df['Votes'] = df['Votes'].str.replace(',','').astype(int)


df['Gross'] = df['Gross'].str.replace('$', '').str.replace('M', '').astype(float)


for col in df.columns:
    missing = df[col].isnull().sum()
    if missing != 0:
        print(f"Column '{col}' has {missing} missing values, dtype is {df[col].dtype}.")

Column 'Content Rating' has 16 missing values, dtype is object.
Column 'Metascore' has 154 missing values, dtype is float64.
Column 'Gross' has 192 missing values, dtype is float64.


df[df['Content Rating'].isnull()]


df['Content Rating'].value_counts().to_frame().T


group_mapping = {
    'G': 'All Ages',
    'Passed': 'All Ages',
    'Approved': 'All Ages',
    'Not Rated': 'All Ages',
    'GP': 'All Ages',
    'PG': 'Teens/PG',
    'TV-PG': 'Teens/PG',
    'PG-13': 'Teens/PG',
    'TV-14': 'Teens/PG',
    '16+': 'Teens/PG',
    'R': 'Adults',
    'TV-MA': 'Adults',
    'NC-17': 'Adults',
    '18+': 'Adults',
    'M/PG': 'Adults',
    'Unrated': 'Adults'
}

# Apply the grouping to the 'Content Rating' column
df['Content'] = df['Content Rating'].map(group_mapping)


df['Content'].value_counts().to_frame()


numeric_cols = [col for col in df.columns if df[col].dtype != object]
object_cols = [col for col in df.columns if df[col].dtype == object]
print(numeric_cols)
print()
print(object_cols)

['Year of Release', 'Duration', 'Rating', 'Metascore', 'Votes', 'Gross']

['Movie Name', 'Content Rating', 'Genre', 'Description', 'Director', 'Stars', 'Content']


_, axes = plt.subplots(4, 3, figsize=(15, 16))

for i, col in enumerate(numeric_cols[:3]):
    sns.histplot(data=df, x=col, ax=axes[0, i])
    axes[0, i].set_xlabel('')
    axes[0, i].set_title(f'Histogram of {col}')
    sns.boxplot(data=df, x=col, ax=axes[1, i])
    axes[1, i].set_xlabel('')
    axes[1, i].set_title(f'Boxplot of {col}')
    if i >=1:
        axes[0, i].set_ylabel('')
for i, col in enumerate(numeric_cols[3:]):
    sns.histplot(data=df, x=col, ax=axes[2, i])
    axes[2, i].set_xlabel('')
    axes[2, i].set_title(f'Histogram of {col}')
    sns.boxplot(data=df, x=col, ax=axes[3, i])
    axes[3, i].set_xlabel('')
    axes[3, i].set_title(f'Boxplot of {col}')
    if i >=1:
        axes[2, i].set_ylabel('')
        
plt.tight_layout()


df[['Votes', 'Gross']].describe()


for col in object_cols:
    unique_values = df[col].unique()
    if len(unique_values) > 5:
        print(f"There are {len(unique_values)} unique values in column '{col}'.")
    else:
        print(f"There are {len(unique_values)} unique values in column '{col}': {unique_values}")

There are 994 unique values in column 'Movie Name'.
There are 17 unique values in column 'Content Rating'.
There are 193 unique values in column 'Genre'.
There are 1000 unique values in column 'Description'.
There are 582 unique values in column 'Director'.
There are 998 unique values in column 'Stars'.
There are 4 unique values in column 'Content': ['Adults' 'Teens/PG' 'All Ages' nan]


highest_rating_movie = df[df['Rating'] == df['Rating'].max()]['Movie Name'].values[0]
highest_rating_year = df[df['Rating'] == df['Rating'].max()]['Year of Release'].values[0]
print(f"Movie with the highest 'Rating': {highest_rating_movie}, made in {highest_rating_year}, with a Rating of {df['Rating'].max()}.")
print("---")
highest_metascore_movie = df[df['Metascore'] == df['Metascore'].max()]['Movie Name'].values[0]
highest_metascore_year = df[df['Metascore'] == df['Metascore'].max()]['Year of Release'].values[0]
print(f"Movie with the highest 'Metascore': {highest_metascore_movie}, made in {highest_metascore_year}, with a Metascore of {df['Metascore'].max()}.")
print("---")
highest_votes_movie = df[df['Votes'] == df['Votes'].max()]['Movie Name'].values[0]
highest_votes_year = df[df['Votes'] == df['Votes'].max()]['Year of Release'].values[0]
print(f"Movie with the highest 'Votes': {highest_votes_movie}, made in {highest_votes_year}, with {df['Votes'].max()/1e6:.2f} million Votes.")
print("---")
highest_gross_movie = df[df['Gross'] == df['Gross'].max()]['Movie Name'].values[0]
highest_gross_year = df[df['Gross'] == df['Gross'].max()]['Year of Release'].values[0]
print(f"Movie with the highest 'Gross': {highest_gross_movie}, made in {highest_gross_year}, with a Gross revenue of ${df['Gross'].max()}M.")
print("---")
longest_duration_movie = df[df['Duration'] == df['Duration'].max()]['Movie Name'].values[0]
longest_duration_year = df[df['Duration'] == df['Duration'].max()]['Year of Release'].values[0]
print(f"Movie with the longest 'Duration': {longest_duration_movie}, made in {longest_duration_year}, with a Duration of {df['Duration'].max()} minutes.")

Movie with the highest 'Rating': The Shawshank Redemption, made in 1994, with a Rating of 9.3.
---
Movie with the highest 'Metascore': The Godfather, made in 1972, with a Metascore of 100.0.
---
Movie with the highest 'Votes': The Shawshank Redemption, made in 1994, with 2.79 million Votes.
---
Movie with the highest 'Gross': Star Wars: Episode VII - The Force Awakens, made in 2015, with a Gross revenue of $936.66M.
---
Movie with the longest 'Duration': Gangs of Wasseypur, made in 2012, with a Duration of 321 minutes.


lowest_rating_movie = df[df['Rating'] == df['Rating'].min()]['Movie Name'].values[0]
lowest_rating_year = df[df['Rating'] == df['Rating'].min()]['Year of Release'].values[0]
print(f"Movie with the lowest 'Rating': {lowest_rating_movie}, made in {lowest_rating_year}, with a Rating of {df['Rating'].min()}.")
print("---")
lowest_metascore_movie = df[df['Metascore'] == df['Metascore'].min()]['Movie Name'].values[0]
lowest_metascore_year = df[df['Metascore'] == df['Metascore'].min()]['Year of Release'].values[0]
print(f"Movie with the lowest 'Metascore': {lowest_metascore_movie}, made in {lowest_metascore_year}, with a Metascore of {df['Metascore'].min()}.")
print("---")
lowest_votes_movie = df[df['Votes'] == df['Votes'].min()]['Movie Name'].values[0]
lowest_votes_year = df[df['Votes'] == df['Votes'].min()]['Year of Release'].values[0]
print(f"Movie with the lowest 'Votes': {lowest_votes_movie}, made in {lowest_votes_year}, with {df['Votes'].min()/1e6:.2f} million Votes.")
print("---")
lowest_gross_movie = df[df['Gross'] == df['Gross'].min()]['Movie Name'].values[0]
lowest_gross_year = df[df['Gross'] == df['Gross'].min()]['Year of Release'].values[0]
print(f"Movie with the lowest 'Gross': {lowest_gross_movie}, made in {lowest_gross_year}, with a Gross revenue of ${df['Gross'].min()}M.")
print("---")
shortest_duration_movie = df[df['Duration'] == df['Duration'].min()]['Movie Name'].values[0]
shortest_duration_year = df[df['Duration'] == df['Duration'].min()]['Year of Release'].values[0]
print(f"Movie with the shortest 'Duration': {shortest_duration_movie}, made in {shortest_duration_year}, with a Duration of {df['Duration'].min()} minutes.")

Movie with the lowest 'Rating': The Blind Side, made in 2009, with a Rating of 7.6.
---
Movie with the lowest 'Metascore': I Am Sam, made in 2001, with a Metascore of 28.0.
---
Movie with the lowest 'Votes': Marty, made in 1955, with 0.03 million Votes.
---
Movie with the lowest 'Gross': Knockin' on Heaven's Door, made in 1997, with a Gross revenue of $0.0M.
---
Movie with the shortest 'Duration': Sherlock Jr., made in 1924, with a Duration of 45 minutes.


df[df['Movie Name'].duplicated(keep=False)].sort_values(by=['Movie Name','Year of Release'])


_, axes = plt.subplots(1, 2, figsize=(12, 5))

sns.countplot(data=df, x='Content', palette='Blues_r', ax=axes[0])
axes[0].set_xlabel('Content Groups')
axes[0].set_ylabel('Count')
axes[0].set_title('Distribution of Content Groups')

sns.countplot(data=df, y='Content Rating', order=df['Content Rating'].value_counts().index, ax=axes[1], palette='Blues_r', hue='Content')
axes[1].set_xlabel('Count')
axes[1].set_ylabel('Content Rating')
axes[1].set_title('Different Types of Content Ratings')
axes[1].legend(loc='lower right')

plt.tight_layout();


plt.figure(figsize=(7,4))

top_10_directors = df['Director'].value_counts().head(10)
sns.barplot(y=top_10_directors.index, x=top_10_directors.values, palette='Blues_r')

for i, v in enumerate(top_10_directors.values):
    plt.text(v + 0.3, i, str(v), ha='center', va='center', color='black', fontsize=9)

plt.ylabel('Director')
plt.title('Top 10 Directors with Most Movies')
plt.xticks([])
plt.show()


top_10_directors_list = top_10_directors.index.to_list()

director_results_df = pd.DataFrame(columns=['Top 10 Director', 'Year of Release', 'Duration', 'Rating', 'Metascore', 'Votes', 'Gross'])

for name in top_10_directors_list:
    director_data = df[df['Director'] == name][['Year of Release', 'Duration', 'Rating', 'Metascore', 'Votes', 'Gross']].mean().to_frame().T.round(1)
    director_data['Top 10 Director'] = name  
    director_results_df = pd.concat([director_results_df, director_data], ignore_index=True)

director_results_df


df['Stars'].head()

0    ['Tim Robbins', 'Morgan Freeman', 'Bob Gunton'...
1    ['Marlon Brando', 'Al Pacino', 'James Caan', '...
2    ['Christian Bale', 'Heath Ledger', 'Aaron Eckh...
3    ['Liam Neeson', 'Ralph Fiennes', 'Ben Kingsley...
4    ['Al Pacino', 'Robert De Niro', 'Robert Duvall...
Name: Stars, dtype: object


df['Stars'] = df['Stars'].apply(ast.literal_eval)

# Create a new column to store the total number of stars for each movie
df['Number of Stars'] = df['Stars'].apply(len)


df['Number of Stars'].value_counts()

4    1000
Name: Number of Stars, dtype: int64


for i in df.index:
    df.at[i, 'Star_1'] = df['Stars'][i][0]
    df.at[i, 'Star_2'] = df['Stars'][i][1]
    df.at[i, 'Star_3'] = df['Stars'][i][2]
    df.at[i, 'Star_4'] = df['Stars'][i][3]


df[['Stars', 'Star_1', 'Star_2', 'Star_3', 'Star_4']].head()


df = df.drop(columns=['Stars', 'Number of Stars'])


all_stars = df['Star_1'].tolist() + df['Star_2'].tolist() + df['Star_3'].tolist() + df['Star_4'].tolist()
top_10_stars = pd.Series(all_stars).value_counts().head(10)
plt.figure(figsize=(8, 4))
sns.barplot(y=top_10_stars.index, x=top_10_stars.values, palette='Blues_r')
plt.xticks(range(0, 17, 1)) 
plt.ylabel('Actor')
plt.xlabel('Number of Appearances')
plt.title(f'Top 10 Actors in {len(all_stars)} Protagonist Roles Across Top 1000 IMDB Movies')
plt.tight_layout();


top_10_stars_list = top_10_stars.index.to_list()

star_averages_list = []

for name in top_10_stars_list:
    star_df = df[(df['Star_4'] == name) | 
                 (df['Star_3'] == name) | 
                 (df['Star_2'] == name) | 
                 (df['Star_1'] == name)][['Year of Release', 'Duration', 'Rating', 'Metascore', 'Votes', 'Gross']]
    
    star_averages = star_df.mean().to_frame().T.round(1)
    star_averages.insert(0, 'Top 10 Stars', name)
    
    star_averages_list.append(star_averages)

stars_summary = pd.concat(star_averages_list, ignore_index=True)
stars_summary


df['Genre'].value_counts().to_frame().head(10)


unique_genres = set()
for genres in df['Genre']:
    unique_genres.update(genres.split(', '))

unique_genres_list = sorted(list(unique_genres))
print(f"There are {len(unique_genres_list)} unique genres among 'Genre' column.\n\n{unique_genres_list}")

There are 21 unique genres among 'Genre' column.

['Action', 'Adventure', 'Animation', 'Biography', 'Comedy', 'Crime', 'Drama', 'Family', 'Fantasy', 'Film-Noir', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'Romance', 'Sci-Fi', 'Sport', 'Thriller', 'War', 'Western']


for genre in unique_genres_list:
    df[genre] = np.where(df['Genre'].str.contains(genre), 1, 0)


pd.set_option('display.max_columns', None)
df.loc[:, 'Action':]


plt.figure(figsize=(8,6))
genre_count = df.loc[:, 'Action':].sum().sort_values(ascending=False)
sns.barplot(x=genre_count.values, y=genre_count.index, palette='Blues_r')
plt.title('Most Common Genres in Movies')
plt.xlabel('Number of Appearances')
plt.ylabel('Genre');


df.head()


corr = df.loc[:,'Movie Name':'Content'].corr()

plt.figure(figsize=(6,4))
sns.heatmap(corr, 
            mask=np.triu(corr), 
            annot=True, annot_kws={'size':9}, 
            linecolor='w', linewidth=0.1, 
            fmt='.1f', 
            cbar_kws={'shrink':0.6}, 
            vmin=-1, vmax=1, center=0, 
            cmap='vlag');


sns.pairplot(df.iloc[:, :12], corner=True, diag_kind='kde', hue ='Content');


_, axes = plt.subplots(1,3, figsize=(15,5))

for i, col in enumerate(['Gross','Votes','Metascore']):
    sns.regplot(data=df, x='Year of Release', y=col, ax=axes[i], line_kws={'color': 'r'})

plt.suptitle('Relationship between Year of Release with Gross, Votes and Metascore');


plt.figure(figsize=(10, 4))
movies_by_year = df.groupby('Year of Release')['Year of Release'].count().reset_index(name='Count')
sns.lineplot(data=movies_by_year, x='Year of Release', y='Count')
plt.title('Number of Movies In Top 1000 IMDB Released Over 100 Years')
plt.ylabel('Number of Movies')
plt.axhline(y=movies_by_year['Count'].median(), color='pink', linestyle='--', label='Overall Median')
plt.axhline(y=movies_by_year[movies_by_year['Year of Release'] > 1980]['Count'].median(), color='red', linestyle='--', label='After 1980 Median')
plt.legend();


print(f"The number of movies in top 1000 IMDB repleased after 1980 is {len(df[df['Year of Release'] > 1980])}.")

The number of movies in top 1000 IMDB repleased after 1980 is 717.


_, axes = plt.subplots(3, 2, figsize=(12, 12))

for i, col in enumerate(['Duration', 'Gross', 'Rating', 'Metascore', 'Votes']):
    sns.lineplot(data=df, x='Year of Release', y=col, ax=axes[i//2, i%2], palette='Blues')
    axes[i//2, i%2].set_title(f'{col} over the Year of Release')

plt.tight_layout();


df['Year Range'] = pd.cut(
    df['Year of Release'],         
    bins=[1920,1930,1940,1950,1960,1970,1980,1990,2000,2010,2023], 
    labels=['1920-1930','1930-1940','1940-1950','1950-1960','1960-1970','1970-1980','1980-1990','1990-2000','2000-2010','2010-2023'] 
)

df.groupby('Year Range')[['Duration','Gross','Rating','Metascore','Votes']].agg(['count','mean']).round(1).T


_, axes = plt.subplots(3, 2, figsize=(12, 12))

for i, col in enumerate(['Duration', 'Gross', 'Rating', 'Metascore', 'Votes']):
    sns.boxplot(data=df, y='Year Range', x=col, ax=axes[i//2, i%2], palette='Blues')
    axes[i//2, i%2].set_title(f'Relationship between {col} and Year Range')
    if i >=1:
        axes[i//2, 1].set_ylabel('')
        axes[i//2, 1].set_yticks([])

plt.tight_layout();


metascore_bins = np.arange(20, 110, 10)
df['Metascore Range'] = pd.cut(df['Metascore'], bins=metascore_bins, labels=[f"{i}-{i+10}" for i in range(20, 100, 10)])

_, axes = plt.subplots(1, 2, figsize=(15, 5))

for i, col in enumerate(['Rating', 'Metascore Range']):
    sns.boxplot(data=df, y='Gross', x=col, ax=axes[i], palette='Blues')

plt.suptitle('Relationship between Gross with Rating and Metascore Range');


sns.boxplot(data=df, x='Metascore Range', y='Rating', palette='Blues_r',hue='Content')
plt.title('Relationship between Metascore Range and Rating splited by Content')
plt.tight_layout();


bin_edges = (0,90, 150, float('inf'))

bin_labels = ['< 90 min', '90 - 150 min', '> 150 min']

df['Duration Bin'] = pd.cut(df['Duration'], bins=bin_edges, labels=bin_labels, include_lowest=True)


average_rating_by_bin = df.groupby('Duration Bin')[['Rating','Metascore', 'Votes', 'Gross']].agg(['count', 'mean']).round(2)
average_rating_by_bin


_, axes = plt.subplots(2, 2, figsize=(10, 8))

for i, col in enumerate(['Rating', 'Metascore', 'Votes', 'Gross']):
    sns.violinplot(data=df, x='Duration Bin', y=col, ax=axes[i//2, i%2], palette='Blues')
    sns.swarmplot(data=df, x='Duration Bin', y=col, ax=axes[i//2, i%2], color='black', alpha=0.5) 
    axes[i//2, i%2].set_title(f'Relationship between {col} and Duration')

plt.tight_layout();


top5_genre = genre_count.index[:5].to_list()
plt.figure(figsize=(16, 4))

for i, col in enumerate(top5_genre, 1):
    plt.subplot(1, 5, i)  
    sns.kdeplot(data=df[df[col] == 1], x='Duration', label=col)
    
    mean_duration = df[df[col] == 1]['Duration'].mean()
    std_duration = df[df[col] == 1]['Duration'].std()

    plt.axvline(mean_duration, color='red', linestyle='dashed', linewidth=1, label='Mean')
    
    plt.ylabel('Density' if i == 1 else '')
    plt.xlabel('Duration' if i == 3 else '')
    plt.legend()
    
    plt.annotate(f'Mean: {mean_duration:.2f}\nStd: {std_duration:.2f}', 
                 xy=(0.6, 0.5), xycoords='axes fraction', fontsize=10, color='black')

plt.suptitle('Duration of Top 5 Genres')
plt.tight_layout();


content = list(df['Content'].dropna().unique())
plt.figure(figsize=(12, 4))

for i, col in enumerate(content, 1):
    plt.subplot(1, 3, i)  
    sns.kdeplot(data=df[df['Content'] == col], x='Duration', label=col)
    
    mean_duration = df[df['Content'] == col]['Duration'].mean()
    std_duration = df[df['Content'] == col]['Duration'].std()
    
    plt.axvline(mean_duration, color='red', linestyle='dashed', linewidth=1, label='Mean')
    plt.ylabel('Density' if i == 1 else '')
    plt.xlabel('Duration' if i == 2 else '')
    plt.legend()
    
    plt.annotate(f'Mean: {mean_duration:.2f}\nStd: {std_duration:.2f}', 
                 xy=(0.5, 0.5), xycoords='axes fraction', fontsize=10, color='black')

plt.suptitle('Duration of Different Content Types')
plt.tight_layout();


description_text = " ".join(df['Description'].dropna()) 

font_path = 'C:\\Windows\\Fonts\\Arial.ttf' 

wordcloud = WordCloud(width=1200, height=500, background_color='white', font_path=font_path).generate(description_text)

plt.figure(figsize=(12, 5))
plt.imshow(wordcloud, 
           #interpolation='bilinear'
          )
plt.axis('off') 
plt.title('Word Cloud of Movie Descriptions');

	Movie Name	Year of Release	Content Rating	Duration	Genre	Rating	Metascore	Description	Director	Stars	Votes	Gross
0	The Shawshank Redemption	1994	R	142	Drama	9.3	82	Over the course of several years, two convicts...	Frank Darabont	[Tim Robbins, Morgan Freeman, Bob Gunton, Will...	2,787,294	$28.34M
1	The Godfather	1972	R	175	Crime, Drama	9.2	100	Don Vito Corleone, head of a mafia family, dec...	Francis Ford Coppola	[Marlon Brando, Al Pacino, James Caan, Diane K...	1,940,977	$134.97M
2	The Dark Knight	2008	PG-13	152	Action, Crime, Drama	9.0	84	When the menace known as the Joker wreaks havo...	Christopher Nolan	[Christian Bale, Heath Ledger, Aaron Eckhart, ...	2,766,703	$534.86M
3	Schindler's List	1993	R	195	Biography, Drama, History	9.0	95	In German-occupied Poland during World War II,...	Steven Spielberg	[Liam Neeson, Ralph Fiennes, Ben Kingsley, Car...	1,402,134	$96.90M
4	The Godfather Part II	1974	R	202	Crime, Drama	9.0	90	The early life and career of Vito Corleone in ...	Francis Ford Coppola	[Al Pacino, Robert De Niro, Robert Duvall, Dia...	1,318,961	$57.30M
...	...	...	...	...	...	...	...	...	...	...	...	...
995	Philomena	2013	PG-13	98	Biography, Comedy, Drama	7.6	77	A world-weary political journalist picks up th...	Stephen Frears	[Judi Dench, Steve Coogan, Sophie Kennedy Clar...	102,453	$37.71M
996	A Very Long Engagement	2004	R	133	Drama, Mystery, Romance	7.6	76	Tells the story of a young woman's relentless ...	Jean-Pierre Jeunet	[Audrey Tautou, Gaspard Ulliel, Jodie Foster, ...	75,092	$6.17M
997	Shine	1996	PG-13	105	Biography, Drama, Music	7.6	87	Pianist David Helfgott, driven by his father a...	Scott Hicks	[Geoffrey Rush, Armin Mueller-Stahl, Justin Br...	55,669	$35.81M
998	The Invisible Man	1933	TV-PG	71	Horror, Sci-Fi	7.6	87	A scientist finds a way of becoming invisible,...	James Whale	[Claude Rains, Gloria Stuart, William Harrigan...	37,918	None
999	Cell 211	2009	N/A	113	Action, Crime, Drama	7.6	N/A	The story of two men on different sides of a p...	Daniel Monzón	[Luis Tosar, Alberto Ammann, Antonio Resines, ...	69,561	None

	Movie Name	Year of Release	Content Rating	Duration	Genre	Rating	Metascore	Description	Director	Stars	Votes	Gross
0	The Shawshank Redemption	1994	R	142	Drama	9.3	82.0	Over the course of several years, two convicts...	Frank Darabont	['Tim Robbins', 'Morgan Freeman', 'Bob Gunton'...	2,787,294	$28.34M
1	The Godfather	1972	R	175	Crime, Drama	9.2	100.0	Don Vito Corleone, head of a mafia family, dec...	Francis Ford Coppola	['Marlon Brando', 'Al Pacino', 'James Caan', '...	1,940,977	$134.97M
2	The Dark Knight	2008	PG-13	152	Action, Crime, Drama	9.0	84.0	When the menace known as the Joker wreaks havo...	Christopher Nolan	['Christian Bale', 'Heath Ledger', 'Aaron Eckh...	2,766,703	$534.86M
3	Schindler's List	1993	R	195	Biography, Drama, History	9.0	95.0	In German-occupied Poland during World War II,...	Steven Spielberg	['Liam Neeson', 'Ralph Fiennes', 'Ben Kingsley...	1,402,134	$96.90M
4	The Godfather Part II	1974	R	202	Crime, Drama	9.0	90.0	The early life and career of Vito Corleone in ...	Francis Ford Coppola	['Al Pacino', 'Robert De Niro', 'Robert Duvall...	1,318,961	$57.30M

	Duration	Rating	Metascore
count	1000.000000	1000.000000	846.000000
mean	124.167000	7.969300	79.024823
std	28.684629	0.275142	11.974158
min	45.000000	7.600000	28.000000
25%	103.000000	7.800000	71.250000
50%	120.000000	7.900000	80.000000
75%	138.250000	8.100000	88.000000
max	321.000000	9.300000	100.000000

	Movie Name	Year of Release	Content Rating	Duration	Genre	Rating	Metascore	Description	Director	Stars	Votes	Gross
59	96	II) (2018	Not Rated	158	Drama, Romance	8.5	NaN	Two high school sweethearts meet at a reunion ...	C. Prem Kumar	['Vijay Sethupathi', 'Adithya Bhaskar', 'Trish...	33,817	NaN
62	Joker	I) (2019	R	122	Crime, Drama, Thriller	8.4	59.0	The rise of Arthur Fleck, from aspiring stand-...	Todd Phillips	['Joaquin Phoenix', 'Robert De Niro', 'Zazie B...	1,390,082	$335.45M
74	Coco	I) (2017	PG	105	Animation, Adventure, Drama	8.4	81.0	Aspiring musician Miguel, confronted with his ...	Lee Unkrich, \nAdrian Molina	['Anthony Gonzalez', 'Gael García Bernal', 'Be...	549,871	$209.73M
165	The Father	I) (2020	PG-13	97	Drama, Mystery	8.2	88.0	A man refuses all assistance from his daughter...	Florian Zeller	['Anthony Hopkins', 'Olivia Colman', 'Mark Gat...	175,346	NaN
237	Inside Out	I) (2015	PG	95	Animation, Adventure, Comedy	8.1	94.0	After young Riley is uprooted from her Midwest...	Pete Docter, \nRonnie Del Carmen	['Amy Poehler', 'Bill Hader', 'Lewis Black', '...	749,855	$356.46M
244	Room	I) (2015	R	118	Drama, Thriller	8.1	86.0	A little boy is held captive in a room with hi...	Lenny Abrahamson	['Brie Larson', 'Jacob Tremblay', 'Sean Bridge...	436,772	$14.68M
246	Spotlight	I) (2015	R	129	Biography, Crime, Drama	8.1	93.0	The true story of how the Boston Globe uncover...	Tom McCarthy	['Mark Ruffalo', 'Michael Keaton', 'Rachel McA...	487,646	$45.06M
250	Rush	I) (2013	R	123	Action, Biography, Drama	8.1	74.0	The merciless 1970s rivalry between Formula On...	Ron Howard	['Daniel Brühl', 'Chris Hemsworth', 'Olivia Wi...	497,244	$26.95M
331	Pink	III) (2016	Not Rated	136	Crime, Drama, Thriller	8.1	NaN	When three young women are implicated in a cri...	Aniruddha Roy Chowdhury	['Amitabh Bachchan', 'Taapsee Pannu', 'Kirti K...	46,849	$1.24M
352	The Revenant	I) (2015	R	156	Action, Adventure, Drama	8.0	76.0	A frontiersman on a fur trading expedition in ...	Alejandro G. Iñárritu	['Leonardo DiCaprio', 'Tom Hardy', 'Will Poult...	840,124	$183.64M

	Movie Name	Year of Release	Content Rating	Duration	Genre	Rating	Metascore	Description	Director	Stars	Votes	Gross
16	777 Charlie	2022	NaN	164	Adventure, Comedy, Drama	8.8	NaN	Dharma is stuck in a rut with his negative and...	Kiranraj K	['Rakshit Shetty', 'Charlie', 'Sangeetha Sring...	36145	NaN
22	Rocketry: The Nambi Effect	2022	NaN	157	Biography, Drama	8.7	NaN	Based on the life of Indian Space Research Org...	Madhavan	['Madhavan', 'Simran', 'Rajit Kapoor', 'Ravi R...	54775	NaN
37	Sita Ramam	2022	NaN	163	Action, Drama, Mystery	8.6	NaN	An orphan soldier, Lieutenant Ram's life chang...	Hanu Raghavapudi	['Dulquer Salmaan', 'Mrunal Thakur', 'Rashmika...	62201	NaN
90	Drishyam 2	2021	NaN	152	Crime, Drama, Thriller	8.4	NaN	A gripping tale of an investigation and a fami...	Jeethu Joseph	['Mohanlal', 'Meena', 'Ansiba', 'Esther Anil']	39493	NaN
130	Vikram	2022	NaN	175	Action, Crime, Thriller	8.3	NaN	A special investigator assigned a case of seri...	Lokesh Kanagaraj	['Kamal Haasan', 'Vijay Sethupathi', 'Fahadh F...	65586	NaN
135	Ayla: The Daughter of War	2017	NaN	125	Biography, Drama, History	8.3	NaN	Sergeant Süleyman finds a little girl on a bat...	Can Ulkay	['Çetin Tekindor', 'Ismail Hacioglu', 'Kyung-j...	42209	NaN
318	Masaan	2015	NaN	109	Drama	8.1	NaN	Along India's Ganges River, four people face p...	Neeraj Ghaywan	['Richa Chadha', 'Sanjay Mishra', 'Vicky Kaush...	29724	NaN
339	Everything's Gonna Be Great	1998	NaN	107	Comedy, Drama, Thriller	8.1	NaN	When Altan swipes prescription drugs from his ...	Ömer Vargi	['Cem Yilmaz', 'Mazhar Alanson', 'Ceyda Düvenc...	26632	NaN
447	Quo Vadis, Aida?	2020	NaN	101	Drama, History, War	8.0	97.0	Aida is a translator for the UN in the small t...	Jasmila Zbanic	['Jasna Djuricic', 'Izudin Bajrovic', 'Boris L...	36530	NaN
454	The Wild Pear Tree	2018	NaN	188	Drama	8.0	86.0	An unpublished writer returns to his hometown ...	Nuri Bilge Ceylan	['Dogu Demirkol', 'Murat Cemcir', 'Bennu Yildi...	26108	0.03
463	G.O.R.A.	2004	NaN	127	Adventure, Comedy, Sci-Fi	8.0	NaN	A slick young Turk kidnapped by extraterrestri...	Ömer Faruk Sorak	['Cem Yilmaz', 'Özge Özberk', 'Ozan Güven', 'S...	65111	NaN
468	The Breath	2009	NaN	128	Action, Drama, Thriller	8.0	NaN	Story of 40-man Turkish task force who must de...	Levent Semerci	['Mete Horozoglu', 'Ilker Kizmaz', 'Birce Akal...	34596	NaN
469	Vizontele	2001	NaN	110	Comedy, Drama	8.0	NaN	Lives of residents in a small, Anatolian villa...	Yilmaz Erdogan, \nÖmer Faruk Sorak	['Yilmaz Erdogan', 'Demet Akbag', 'Altan Erkek...	37907	NaN
883	Perfect Strangers	2016	NaN	96	Comedy, Drama	7.7	NaN	Seven long-time friends meet for dinner. They ...	Paolo Genovese	['Giuseppe Battiston', 'Anna Foglietta', 'Marc...	68865	NaN
916	Fireworks	1997	NaN	103	Crime, Drama, Romance	7.7	83.0	Nishi leaves the police in the face of harrowi...	Takeshi Kitano	['Takeshi Kitano', 'Kayoko Kishimoto', 'Ren Ôs...	32065	0.23
999	Cell 211	2009	NaN	113	Action, Crime, Drama	7.6	NaN	The story of two men on different sides of a p...	Daniel Monzón	['Luis Tosar', 'Alberto Ammann', 'Antonio Resi...	69561	NaN

Web Scraping, Exploratory Data Analysis, and Predictive Modeling ¶

Table of Contents¶

Part 1: Scraping Top 1000 IMDB Movies Data¶

Part 2: Exploratory Data Analysis (EDA)¶

Goals¶

Part 1: Scraping Top 1000 IMDB Movies Data¶

1. Import Libraries¶

2. Data Crawling¶

3. DataFrame Building¶

4. Description of the Dataset¶

Part 2: Exploratory Data Analysis¶

1. Import More Libraries¶

2. Initial Summaries¶

3. Convert Data Types¶

`Year of Release`¶

`Votes`¶

`Gross`¶

4. Check Missing Values¶

`Content Rating`¶

5. Visualization and In-depth Analysis¶

5.1. Numerical Variables Analysis¶

5.2. Categorical Variables Analysis¶

`Movie Name`¶

`Content Rating` and `Content`¶

`Directors`¶

`Stars`¶

`Genre`¶

5.3. Correlation Analysis¶

5.4. Trends and Time Series Analysis¶

5.5. Rating Factor Comparison¶

5.6. Duration Factor Comparison¶

5.7. Text Data Analysis¶

6. Re-summarize Key Insights¶

	Movie Name	Year of Release	Content Rating	Genre	Description	Director	Stars	Votes	Gross
count	1000	1000	984	1000	1000	1000	1000	1000	808
unique	994	123	16	193	1000	582	998	1000	704
top	Beauty and the Beast	2014	R	Drama	Over the course of several years, two convicts...	Alfred Hitchcock	['Prabhas', 'Rana Daggubati', 'Anushka Shetty'...	2,787,294	$0.01M
freq	2	28	358	92	1	12	2	1	8

Content Rating	Description
R	Restricted, children under 17 require accompanying parents or adult guardians.
Not Rated	Movies that have not been assigned a specific rating.
PG-13	Parents strongly cautioned, some material may be inappropriate for children under 13.
PG	Parental guidance suggested, some material may not be suitable for children.
Passed	Movies that have been passed for all audiences.
G	Suitable for all ages and audiences.
Approved	Movies that have been approved for all audiences.
TV-MA	Intended for mature audiences only, may contain graphic violence, explicit sex, or strong language.
TV-PG	Parental guidance suggested for some material.
TV-14	Parents strongly cautioned, some material may be inappropriate for children under 14.
GP	Suitable for general audiences.
16+	Intended for audiences aged 16 and above.
NC-17	Intended for adults only, children under 17 not admitted.
18+	Intended for adults only, children under 18 not admitted.
M/PG	Intended for mature audiences, parental guidance suggested.
Unrated	Movies with an unknown or missing rating.

	Votes	Gross
count	1.000000e+03	808.000000
mean	3.203577e+05	70.264319
std	3.885927e+05	115.415159
min	2.589400e+04	0.000000
25%	6.260075e+04	3.140000
50%	1.586355e+05	23.250000
75%	4.443552e+05	83.417500
max	2.787294e+06	936.660000

	Movie Name	Year of Release	Content Rating	Duration	Genre	Rating	Metascore	Description	Director	Stars	Votes	Gross	Content
283	All Quiet on the Western Front	1930	Passed	152	Drama, War	8.1	91.0	A German youth eagerly enters World War I, but...	Lewis Milestone	['Lew Ayres', 'Louis Wolheim', 'John Wray', 'A...	65985	3.27	All Ages
612	All Quiet on the Western Front	2022	R	148	Action, Drama, War	7.8	76.0	A young German soldier's terrifying experience...	Edward Berger	['Felix Kammerer', 'Albrecht Schuch', 'Aaron H...	218740	NaN	Adults
588	Beauty and the Beast	1946	Not Rated	93	Drama, Fantasy, Romance	7.9	92.0	A beautiful young woman takes her father's pla...	Jean Cocteau, \nRené Clément	['Jean Marais', 'Josette Day', 'Mila Parély', ...	27430	0.30	All Ages
378	Beauty and the Beast	1991	G	84	Animation, Family, Fantasy	8.0	95.0	A prince cursed to spend his days as a hideous...	Gary Trousdale, \nKirk Wise	["Paige O'Hara", 'Robby Benson', 'Jesse Corti'...	468702	218.97	All Ages
138	Drishyam	2013	Not Rated	160	Crime, Drama, Thriller	8.3	NaN	A man goes to extreme lengths to save his fami...	Jeethu Joseph	['Mohanlal', 'Meena', 'Asha Sharath', 'Ansiba']	42947	NaN	All Ages
195	Drishyam	2015	Not Rated	163	Crime, Drama, Mystery	8.2	NaN	Desperate measures are taken by a man who trie...	Nishikant Kamat	['Ajay Devgn', 'Shriya Saran', 'Tabu', 'Rajat ...	89968	0.74	All Ages
90	Drishyam 2	2021	NaN	152	Crime, Drama, Thriller	8.4	NaN	A gripping tale of an investigation and a fami...	Jeethu Joseph	['Mohanlal', 'Meena', 'Ansiba', 'Esther Anil']	39493	NaN	NaN
187	Drishyam 2	2022	Not Rated	140	Crime, Drama, Mystery	8.2	NaN	A gripping tale of an investigation and a fami...	Abhishek Pathak	['Ajay Devgn', 'Akshaye Khanna', 'Tabu', 'Shri...	39523	NaN	All Ages
908	Scarface	1932	PG	93	Action, Crime, Drama	7.7	90.0	An ambitious and nearly insane violent gangste...	Howard Hawks, \nRichard Rosson	['Paul Muni', 'Ann Dvorak', 'Karen Morley', 'O...	29354	NaN	Teens/PG
100	Scarface	1983	18+	170	Crime, Drama	8.3	65.0	In 1980 Miami, a determined Cuban immigrant ta...	Brian De Palma	['Al Pacino', 'Michelle Pfeiffer', 'Steven Bau...	880217	45.60	Adults
672	The Girl with the Dragon Tattoo	2009	R	152	Crime, Drama, Mystery	7.8	76.0	A journalist is aided by a young female hacker...	Niels Arden Oplev	['Michael Nyqvist', 'Noomi Rapace', 'Ewa Fröli...	220487	10.10	Adults
617	The Girl with the Dragon Tattoo	2011	R	158	Crime, Drama, Mystery	7.8	71.0	Journalist Mikael Blomkvist is aided in his se...	David Fincher	['Daniel Craig', 'Rooney Mara', 'Christopher P...	479358	102.52	Adults

	Top 10 Director	Year of Release	Duration	Rating	Metascore	Votes	Gross
0	Alfred Hitchcock	1951.2	110.5	8.1	91.6	249578.2	13.2
1	Akira Kurosawa	1963.4	135.4	8.2	92.6	102501.4	1.1
2	Steven Spielberg	1989.7	142.3	8.1	79.8	779117.9	206.7
3	Hayao Miyazaki	1996.2	113.0	8.0	84.1	284663.5	5.6
4	Martin Scorsese	1995.6	145.0	8.1	82.9	785760.7	53.8
5	Woody Allen	1987.8	96.0	7.8	82.4	149904.3	30.7
6	Christopher Nolan	2010.6	144.7	8.5	79.0	1582150.6	242.2
7	Ingmar Bergman	1966.2	100.9	8.1	89.2	72364.7	2.7
8	Billy Wilder	1952.6	113.8	8.1	85.6	132423.3	11.8
9	Quentin Tarantino	2006.0	143.5	8.2	78.6	1212806.9	90.9

	Stars	Star_1	Star_2	Star_3	Star_4
0	[Tim Robbins, Morgan Freeman, Bob Gunton, Will...	Tim Robbins	Morgan Freeman	Bob Gunton	William Sadler
1	[Marlon Brando, Al Pacino, James Caan, Diane K...	Marlon Brando	Al Pacino	James Caan	Diane Keaton
2	[Christian Bale, Heath Ledger, Aaron Eckhart, ...	Christian Bale	Heath Ledger	Aaron Eckhart	Michael Caine
3	[Liam Neeson, Ralph Fiennes, Ben Kingsley, Car...	Liam Neeson	Ralph Fiennes	Ben Kingsley	Caroline Goodall
4	[Al Pacino, Robert De Niro, Robert Duvall, Dia...	Al Pacino	Robert De Niro	Robert Duvall	Diane Keaton

	Top 10 Stars	Year of Release	Duration	Rating	Metascore	Votes	Gross
0	Robert De Niro	1991.2	150.3	8.1	81.2	574744.0	59.5
1	Tom Hanks	2001.5	128.9	8.1	79.9	823862.2	217.8
2	Al Pacino	1988.7	155.4	8.1	80.1	565620.6	47.9
3	Matt Damon	2007.5	132.6	8.0	80.1	693448.8	157.1
4	Christian Bale	2006.6	132.9	8.0	72.9	932189.5	146.9
5	Leonardo DiCaprio	2007.5	154.1	8.1	74.9	1189019.3	186.3
6	Brad Pitt	2005.9	135.5	8.0	74.6	970647.5	91.0
7	James Stewart	1948.5	118.5	8.1	90.7	182592.1	11.1
8	Humphrey Bogart	1947.7	107.8	7.9	87.1	126404.0	6.2
9	Aamir Khan	2005.2	173.0	8.2	75.5	156289.8	4.8

	Action	Adventure	Animation	Biography	Comedy	Crime	Drama	Family	Fantasy	Film-Noir	History	Horror	Music	Musical	Mystery	Romance	Sci-Fi	Sport	Thriller	War	Western
0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
995	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
996	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0
997	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0
998	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0
999	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0

	Year Range	1920-1930	1930-1940	1940-1950	1950-1960	1960-1970	1970-1980	1980-1990	1990-2000	2000-2010	2010-2023
Duration	count	11.0	31.0	38.0	64.0	65.0	73.0	88.0	150.0	222.0	257.0
Duration	mean	93.7	103.7	107.1	118.7	127.8	123.8	122.2	124.4	124.4	131.6
Gross	count	9.0	21.0	17.0	41.0	43.0	56.0	81.0	138.0	205.0	197.0
Gross	mean	2.0	24.2	5.0	13.9	33.7	58.6	60.1	62.7	79.7	106.7
Rating	count	11.0	31.0	38.0	64.0	65.0	73.0	88.0	150.0	222.0	257.0
Rating	mean	8.1	8.0	8.0	8.0	8.0	8.0	7.9	8.0	7.9	8.0
Metascore	count	6.0	25.0	27.0	52.0	56.0	64.0	79.0	136.0	200.0	201.0
Metascore	mean	94.8	92.1	91.4	88.9	84.0	82.2	76.5	75.2	74.9	78.0
Votes	count	11.0	31.0	38.0	64.0	65.0	73.0	88.0	150.0	222.0	257.0
Votes	mean	86274.0	106548.4	110300.8	132401.0	133842.1	287400.0	298167.3	431860.6	426033.9	342785.7

	Action	Adventure	Animation	Biography	Comedy	Crime	Drama	Family	Fantasy	Film-Noir	History	Horror	Music	Musical	Mystery	Romance	Sci-Fi	Sport	Thriller	War	Western
0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
995	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
996	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0
997	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0
998	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0
999	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0

Web Scraping, Exploratory Data Analysis, and Predictive Modeling ¶

Table of Contents¶

Part 1: Scraping Top 1000 IMDB Movies Data¶

Part 2: Exploratory Data Analysis (EDA)¶

Goals¶

Part 1: Scraping Top 1000 IMDB Movies Data¶

1. Import Libraries¶

2. Data Crawling¶

3. DataFrame Building¶

4. Description of the Dataset¶

Part 2: Exploratory Data Analysis¶

1. Import More Libraries¶

2. Initial Summaries¶

3. Convert Data Types¶

Year of Release¶

Votes¶

Gross¶

4. Check Missing Values¶

Content Rating¶

5. Visualization and In-depth Analysis¶

5.1. Numerical Variables Analysis¶

5.2. Categorical Variables Analysis¶

Movie Name¶

Content Rating and Content¶

Directors¶

Stars¶

Genre¶

5.3. Correlation Analysis¶

5.4. Trends and Time Series Analysis¶

5.5. Rating Factor Comparison¶

5.6. Duration Factor Comparison¶

5.7. Text Data Analysis¶

6. Re-summarize Key Insights¶

`Year of Release`¶

`Votes`¶

`Gross`¶

`Content Rating`¶

`Movie Name`¶

`Content Rating` and `Content`¶

`Directors`¶

`Stars`¶

`Genre`¶

	Action	Adventure	Animation	Biography	Comedy	Crime	Drama	Family	Fantasy	Film-Noir	History	Horror	Music	Musical	Mystery	Romance	Sci-Fi	Sport	Thriller	War	Western
0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
995	0	0	0	1	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
996	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0
997	0	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0
998	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0
999	1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0